mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-09-10 04:00:35 +00:00
1607 lines
51 KiB
Markdown
1607 lines
51 KiB
Markdown
# 🧠 SemantiCore
|
|
|
|
<div align="center">
|
|
|
|
[](https://www.python.org/downloads/)
|
|
[](https://opensource.org/licenses/MIT)
|
|
[](https://badge.fury.io/py/semanticore)
|
|
[](https://pepy.tech/project/semanticore)
|
|
[](https://hub.docker.com/r/semanticore/semanticore)
|
|
[](https://kubernetes.io/)
|
|
[](https://github.com/semanticore/semanticore)
|
|
[](https://github.com/semanticore/semanticore/graphs/contributors)
|
|
|
|
**🚀 The Ultimate Open Source Semantic Intelligence Toolkit**
|
|
|
|
*Transform any data format into intelligent, contextual knowledge graphs, embeddings, and semantic structures that power next-generation AI applications, RAG systems, and intelligent agents.*
|
|
|
|
[📖 Documentation](https://semanticore.readthedocs.io/) • [🚀 Quick Start](#-quick-start) • [💡 Examples](#-examples) • [🤝 Community](#-community) • [🔧 API Reference](https://semanticore.readthedocs.io/api/)
|
|
|
|
</div>
|
|
|
|
---
|
|
|
|
## 🌟 What is SemantiCore?
|
|
|
|
SemantiCore is the most comprehensive open-source semantic intelligence platform that transforms raw, unstructured data from **any source** into intelligent, contextual knowledge. Built for developers, researchers, and enterprises, it bridges the gap between chaotic data and AI-ready semantic understanding.
|
|
|
|
> **"The missing link between your data and AI — turning unstructured chaos into structured, intelligent semantic knowledge."**
|
|
|
|
### 🎯 Why Choose SemantiCore?
|
|
|
|
<table>
|
|
<tr>
|
|
<td width="50%">
|
|
|
|
**📊 Universal Data Intelligence**
|
|
- 60+ file formats and data sources
|
|
- Real-time streaming and batch processing
|
|
- Multi-modal content understanding
|
|
- Recursive archive processing
|
|
|
|
**🧠 Advanced Semantic AI**
|
|
- Multi-layered semantic understanding
|
|
- Automatic knowledge graph construction
|
|
- Context-aware relationship extraction
|
|
- Intelligent ontology generation
|
|
|
|
</td>
|
|
<td width="50%">
|
|
|
|
**🔧 Context Engineering**
|
|
- Advanced context building and preservation
|
|
- Cross-document context linking
|
|
- Temporal context modeling
|
|
- Hierarchical context structures
|
|
|
|
**🚀 Production Ready**
|
|
- Distributed processing architecture
|
|
- Real-time streaming capabilities
|
|
- Enterprise-grade scalability
|
|
- Comprehensive monitoring and analytics
|
|
|
|
</td>
|
|
</tr>
|
|
</table>
|
|
|
|
---
|
|
|
|
## 🔧 Core Modules Overview
|
|
|
|
SemantiCore is built with a modular architecture that provides comprehensive semantic intelligence capabilities:
|
|
|
|
### 📊 **Data Processing Modules**
|
|
- **[Document Processor](#-document-processing-module)** - PDF, DOCX, XLSX, PPTX, LaTeX, EPUB and more
|
|
- **[Web Processor](#-web--feed-processing-module)** - HTML, XML, RSS/Atom feeds, sitemap processing
|
|
- **[Structured Data Processor](#-structured-data-processing-module)** - JSON, CSV, YAML, XML, Parquet, Avro
|
|
- **[Archive Processor](#-archive-processing-module)** - ZIP, TAR, RAR, 7Z with recursive extraction
|
|
- **[Email Processor](#-email-processing-module)** - EML, MSG, MBOX, PST archives
|
|
- **[Code Repository Processor](#-code-repository-processing-module)** - Git repos, documentation, README files
|
|
- **[Scientific Format Processor](#-scientific-format-processing-module)** - BibTeX, EndNote, JATS XML, RIS
|
|
|
|
### 🧠 **Semantic Intelligence Modules**
|
|
- **[Entity Extraction Engine](#-entity-extraction-engine)** - Named entities, relationships, events
|
|
- **[Triple Generation Engine](#-triple-generation-engine)** - Automatic RDF triple extraction
|
|
- **[Ontology Builder](#-ontology-builder)** - Dynamic ontology creation and mapping
|
|
- **[Context Engineering System](#-context-engineering-system)** - Advanced context building and preservation
|
|
- **[Relationship Mapper](#-relationship-mapper)** - Cross-document relationship detection
|
|
- **[Semantic Reasoning Engine](#-semantic-reasoning-engine)** - Inductive, deductive, abductive reasoning
|
|
|
|
### 🕸️ **Knowledge Graph Modules**
|
|
- **[Knowledge Graph Builder](#-knowledge-graph-builder)** - Automated graph construction
|
|
- **[Graph Analytics Engine](#-graph-analytics-engine)** - Centrality, community detection, path finding
|
|
- **[SPARQL Query Generator](#-sparql-query-generator)** - Automatic semantic query generation
|
|
- **[Triple Store Manager](#-triple-store-manager)** - Blazegraph, Virtuoso, Jena, GraphDB integration
|
|
- **[Graph Database Connector](#-graph-database-connector)** - Neo4j, KuzuDB, ArangoDB, Neptune support
|
|
|
|
### 📈 **Vector & Embedding Modules**
|
|
- **[Semantic Embedder](#-semantic-embedder)** - Context-aware embeddings
|
|
- **[Vector Store Manager](#-vector-store-manager)** - Pinecone, Weaviate, Chroma, Qdrant support
|
|
- **[Semantic Chunker](#-semantic-chunker)** - Intelligent content segmentation
|
|
- **[Similarity Engine](#-similarity-engine)** - Semantic similarity and duplicate detection
|
|
|
|
### 🌊 **Streaming & Real-time Modules**
|
|
- **[Stream Processor](#-stream-processor)** - Kafka, RabbitMQ, Pulsar integration
|
|
- **[Live Feed Monitor](#-live-feed-monitor)** - Real-time RSS/Atom feed processing
|
|
- **[Web Monitor](#-web-monitor)** - Website change detection and processing
|
|
- **[Event Stream Analyzer](#-event-stream-analyzer)** - Real-time event processing
|
|
|
|
### 🔍 **Analysis & Intelligence Modules**
|
|
- **[Content Analyzer](#-content-analyzer)** - Topic modeling, sentiment analysis
|
|
- **[Language Intelligence](#-language-intelligence)** - 100+ language detection and processing
|
|
- **[Temporal Analyzer](#-temporal-analyzer)** - Time-aware semantic understanding
|
|
- **[Cross-Reference Resolver](#-cross-reference-resolver)** - Link resolution across documents
|
|
|
|
### 🔧 **Infrastructure & Utility Modules**
|
|
- **[Pipeline Builder](#-pipeline-builder)** - Custom processing pipeline creation
|
|
- **[Quality Assurance](#-quality-assurance)** - Validation and quality metrics
|
|
- **[Monitoring Dashboard](#-monitoring-dashboard)** - Real-time analytics and alerts
|
|
- **[Export Engine](#-export-engine)** - Multiple format export capabilities
|
|
|
|
---
|
|
|
|
## 🚀 Quick Start
|
|
|
|
### 📦 Installation
|
|
|
|
```bash
|
|
# Complete installation with all modules
|
|
pip install "semanticore[all]"
|
|
|
|
# Lightweight core installation
|
|
pip install semanticore
|
|
|
|
# Specific module support
|
|
pip install "semanticore[pdf,web,feeds,office,scientific]"
|
|
|
|
# Development installation
|
|
git clone https://github.com/semanticore/semanticore.git
|
|
cd semanticore
|
|
pip install -e ".[dev]"
|
|
```
|
|
|
|
### ⚡ Transform Any Data in 60 Seconds
|
|
|
|
```python
|
|
from semanticore import SemantiCore
|
|
|
|
# Initialize with your preferred providers
|
|
core = SemantiCore(
|
|
llm_provider="openai", # or "anthropic", "huggingface", "ollama"
|
|
embedding_model="text-embedding-3-large",
|
|
vector_store="pinecone", # or "weaviate", "chroma", "qdrant"
|
|
graph_db="neo4j" # or "blazegraph", "virtuoso", "janusgraph"
|
|
)
|
|
|
|
# Process any combination of data sources
|
|
data_sources = [
|
|
"research_papers/", # Directory of PDFs
|
|
"financial_reports.xlsx", # Excel spreadsheets
|
|
"https://news.example.com/rss", # RSS feeds
|
|
"meeting_notes.docx", # Word documents
|
|
"data_exports.json", # JSON files
|
|
"https://blog.example.com", # Web pages
|
|
"emails.mbox", # Email archives
|
|
"code_repos/", # Git repositories
|
|
]
|
|
|
|
# One command to build intelligent knowledge base
|
|
knowledge_base = core.build_knowledge_base(
|
|
sources=data_sources,
|
|
enable_context_engineering=True,
|
|
preserve_relationships=True,
|
|
generate_ontology=True
|
|
)
|
|
|
|
# Instant results
|
|
print(f"📊 Processed: {len(knowledge_base.documents)} documents")
|
|
print(f"🧠 Extracted: {len(knowledge_base.entities)} entities")
|
|
print(f"🔗 Generated: {len(knowledge_base.triples)} semantic triples")
|
|
print(f"📈 Created: {len(knowledge_base.embeddings)} vector embeddings")
|
|
print(f"🕸️ Built: {knowledge_base.knowledge_graph.node_count} node knowledge graph")
|
|
|
|
# Intelligent querying
|
|
results = knowledge_base.query(
|
|
"What are the main themes and relationships in this data?",
|
|
include_context=True,
|
|
reasoning_depth=2
|
|
)
|
|
|
|
for result in results:
|
|
print(f"📝 {result.content}")
|
|
print(f"🔗 Related: {result.related_entities}")
|
|
print(f"📊 Confidence: {result.confidence:.2%}")
|
|
```
|
|
|
|
---
|
|
|
|
## 🔧 Core Processing Modules
|
|
|
|
### 📄 Document Processing Module
|
|
|
|
Handle complex document formats with full semantic understanding:
|
|
|
|
```python
|
|
from semanticore.processors import DocumentProcessor
|
|
|
|
# Initialize with comprehensive extraction capabilities
|
|
doc_processor = DocumentProcessor(
|
|
extract_tables=True,
|
|
extract_images=True,
|
|
extract_metadata=True,
|
|
preserve_structure=True,
|
|
ocr_enabled=True,
|
|
language_detection=True
|
|
)
|
|
|
|
# Process various document formats
|
|
processed_docs = []
|
|
|
|
# PDF processing with advanced extraction
|
|
pdf_content = doc_processor.process_pdf(
|
|
"complex_report.pdf",
|
|
extract_annotations=True,
|
|
preserve_formatting=True
|
|
)
|
|
|
|
# Office document processing
|
|
docx_content = doc_processor.process_docx("document.docx")
|
|
pptx_content = doc_processor.process_pptx("presentation.pptx")
|
|
xlsx_content = doc_processor.process_excel("spreadsheet.xlsx")
|
|
|
|
# Scientific document processing
|
|
latex_content = doc_processor.process_latex("research_paper.tex")
|
|
epub_content = doc_processor.process_epub("book.epub")
|
|
|
|
# Extract semantic intelligence from all documents
|
|
for content in [pdf_content, docx_content, pptx_content, xlsx_content]:
|
|
# Build context-aware semantics
|
|
semantic_structure = core.extract_semantic_structure(content)
|
|
|
|
# Generate knowledge triples
|
|
triples = core.generate_triples(semantic_structure)
|
|
|
|
# Create contextual embeddings
|
|
embeddings = core.create_contextual_embeddings(content.chunks)
|
|
|
|
processed_docs.append({
|
|
'content': content,
|
|
'semantics': semantic_structure,
|
|
'triples': triples,
|
|
'embeddings': embeddings
|
|
})
|
|
```
|
|
|
|
### 🌐 Web & Feed Processing Module
|
|
|
|
Real-time web content and feed processing with semantic understanding:
|
|
|
|
```python
|
|
from semanticore.processors import WebProcessor, FeedProcessor
|
|
|
|
# Advanced web content processor
|
|
web_processor = WebProcessor(
|
|
respect_robots=True,
|
|
extract_metadata=True,
|
|
follow_redirects=True,
|
|
max_depth=3,
|
|
content_extraction_mode="semantic",
|
|
javascript_rendering=True
|
|
)
|
|
|
|
# Intelligent feed processor
|
|
feed_processor = FeedProcessor(
|
|
update_interval="5m",
|
|
deduplicate=True,
|
|
extract_full_content=True,
|
|
sentiment_analysis=True,
|
|
topic_extraction=True
|
|
)
|
|
|
|
# Process web content with context preservation
|
|
webpage = web_processor.process_url("https://example.com/article")
|
|
semantic_content = core.extract_semantics(
|
|
webpage.content,
|
|
preserve_context=True,
|
|
extract_relationships=True
|
|
)
|
|
|
|
# Monitor multiple RSS feeds with intelligent processing
|
|
news_feeds = [
|
|
"https://feeds.feedburner.com/TechCrunch",
|
|
"https://rss.cnn.com/rss/edition.rss",
|
|
"https://feeds.reuters.com/reuters/topNews",
|
|
"https://feeds.bbci.co.uk/news/rss.xml"
|
|
]
|
|
|
|
# Subscribe to feeds with semantic processing
|
|
for feed_url in news_feeds:
|
|
feed_processor.subscribe(
|
|
feed_url,
|
|
enable_semantic_analysis=True,
|
|
extract_entities=True,
|
|
build_context=True
|
|
)
|
|
|
|
# Process feed items with contextual understanding
|
|
async for item in feed_processor.stream_items():
|
|
# Extract semantic intelligence
|
|
semantic_data = core.extract_semantics(item.content)
|
|
|
|
# Build contextual relationships
|
|
contextual_triples = core.generate_contextual_triples(semantic_data)
|
|
|
|
# Update knowledge graph with new intelligence
|
|
knowledge_graph.add_triples(contextual_triples)
|
|
|
|
print(f"📰 Processed: {item.title}")
|
|
print(f"🏷️ Topics: {item.topics}")
|
|
print(f"😊 Sentiment: {item.sentiment}")
|
|
```
|
|
|
|
### 📊 Structured Data Processing Module
|
|
|
|
Transform structured and semi-structured data into semantic knowledge:
|
|
|
|
```python
|
|
from semanticore.processors import StructuredDataProcessor
|
|
|
|
# Initialize with intelligent schema detection
|
|
structured_processor = StructuredDataProcessor(
|
|
infer_schema=True,
|
|
extract_relationships=True,
|
|
generate_ontology=True,
|
|
preserve_hierarchies=True,
|
|
detect_patterns=True
|
|
)
|
|
|
|
# Process various structured formats
|
|
data_files = [
|
|
"customer_data.json",
|
|
"sales_records.csv",
|
|
"configuration.yaml",
|
|
"product_catalog.xml",
|
|
"analytics_data.parquet"
|
|
]
|
|
|
|
processed_data = []
|
|
|
|
for file_path in data_files:
|
|
# Determine format and process accordingly
|
|
if file_path.endswith('.json'):
|
|
data = structured_processor.process_json(file_path)
|
|
elif file_path.endswith('.csv'):
|
|
data = structured_processor.process_csv(file_path)
|
|
elif file_path.endswith('.yaml'):
|
|
data = structured_processor.process_yaml(file_path)
|
|
elif file_path.endswith('.xml'):
|
|
data = structured_processor.process_xml(file_path)
|
|
elif file_path.endswith('.parquet'):
|
|
data = structured_processor.process_parquet(file_path)
|
|
|
|
# Extract semantic relationships
|
|
schema = structured_processor.generate_semantic_schema(data)
|
|
relationships = structured_processor.extract_data_relationships(data)
|
|
ontology = structured_processor.create_data_ontology(schema, relationships)
|
|
|
|
# Generate contextual triples
|
|
triples = structured_processor.generate_semantic_triples(data, schema)
|
|
|
|
processed_data.append({
|
|
'data': data,
|
|
'schema': schema,
|
|
'relationships': relationships,
|
|
'ontology': ontology,
|
|
'triples': triples
|
|
})
|
|
|
|
# Merge all structured data into unified knowledge graph
|
|
unified_graph = core.merge_data_graphs([item['triples'] for item in processed_data])
|
|
```
|
|
|
|
### 🔧 Context Engineering System
|
|
|
|
Advanced context building and preservation across all data sources:
|
|
|
|
```python
|
|
from semanticore.context import ContextEngineer
|
|
|
|
# Initialize context engineering system
|
|
context_engineer = ContextEngineer(
|
|
context_window_size=8192,
|
|
hierarchical_context=True,
|
|
cross_document_linking=True,
|
|
temporal_context=True,
|
|
semantic_clustering=True
|
|
)
|
|
|
|
# Build comprehensive context from multiple sources
|
|
context_sources = [
|
|
"project_documents/",
|
|
"meeting_transcripts/",
|
|
"email_threads/",
|
|
"code_repositories/",
|
|
"research_papers/"
|
|
]
|
|
|
|
# Create multi-layered context structure
|
|
context_graph = context_engineer.build_context_graph(context_sources)
|
|
|
|
# Advanced context features
|
|
context_features = {
|
|
# Hierarchical context preservation
|
|
'document_hierarchy': context_engineer.build_document_hierarchy(context_sources),
|
|
|
|
# Temporal context mapping
|
|
'temporal_context': context_engineer.extract_temporal_context(context_sources),
|
|
|
|
# Cross-reference context
|
|
'cross_references': context_engineer.build_cross_reference_map(context_sources),
|
|
|
|
# Semantic context clustering
|
|
'semantic_clusters': context_engineer.create_semantic_clusters(context_sources),
|
|
|
|
# Contextual embeddings
|
|
'context_embeddings': context_engineer.generate_contextual_embeddings(context_sources)
|
|
}
|
|
|
|
# Query with full context awareness
|
|
contextual_results = context_engineer.query_with_context(
|
|
query="How do these documents relate to each other?",
|
|
context_depth=3,
|
|
include_temporal=True,
|
|
include_cross_references=True
|
|
)
|
|
|
|
# Context-aware response generation
|
|
for result in contextual_results:
|
|
print(f"📄 Document: {result.source}")
|
|
print(f"🔗 Context: {result.context_summary}")
|
|
print(f"⏰ Temporal: {result.temporal_context}")
|
|
print(f"🌐 Related: {result.related_documents}")
|
|
```
|
|
|
|
### 📈 Semantic Embedder
|
|
|
|
Create context-aware embeddings optimized for semantic understanding:
|
|
|
|
```python
|
|
from semanticore.embeddings import SemanticEmbedder
|
|
|
|
# Initialize advanced semantic embedder
|
|
embedder = SemanticEmbedder(
|
|
model="text-embedding-3-large",
|
|
dimension=1536,
|
|
preserve_context=True,
|
|
semantic_chunking=True,
|
|
hierarchical_embedding=True,
|
|
multi_modal_support=True
|
|
)
|
|
|
|
# Advanced semantic chunking strategies
|
|
chunking_strategies = {
|
|
'semantic': embedder.semantic_chunk,
|
|
'hierarchical': embedder.hierarchical_chunk,
|
|
'context_aware': embedder.context_aware_chunk,
|
|
'topic_based': embedder.topic_based_chunk
|
|
}
|
|
|
|
# Process documents with multiple chunking strategies
|
|
documents = load_documents_from_sources(data_sources)
|
|
embedding_results = {}
|
|
|
|
for strategy_name, chunk_function in chunking_strategies.items():
|
|
# Create semantic chunks
|
|
chunks = chunk_function(documents, preserve_relationships=True)
|
|
|
|
# Generate contextual embeddings
|
|
embeddings = embedder.generate_contextual_embeddings(
|
|
chunks,
|
|
include_metadata=True,
|
|
preserve_hierarchy=True
|
|
)
|
|
|
|
embedding_results[strategy_name] = {
|
|
'chunks': chunks,
|
|
'embeddings': embeddings,
|
|
'metadata': embedder.extract_chunk_metadata(chunks)
|
|
}
|
|
|
|
# Store in vector database with semantic metadata
|
|
vector_store = core.get_vector_store("pinecone")
|
|
for strategy, results in embedding_results.items():
|
|
vector_store.store_embeddings(
|
|
chunks=results['chunks'],
|
|
embeddings=results['embeddings'],
|
|
metadata=results['metadata'],
|
|
strategy=strategy
|
|
)
|
|
|
|
# Advanced semantic search with context
|
|
query = "artificial intelligence applications in healthcare data analysis"
|
|
search_results = vector_store.semantic_search(
|
|
query=query,
|
|
top_k=20,
|
|
include_context=True,
|
|
re_rank=True,
|
|
diversity_threshold=0.7
|
|
)
|
|
|
|
for result in search_results:
|
|
print(f"📝 Content: {result.content[:200]}...")
|
|
print(f"🎯 Relevance: {result.score:.3f}")
|
|
print(f"🔗 Context: {result.context_summary}")
|
|
print(f"📊 Metadata: {result.metadata}")
|
|
```
|
|
|
|
---
|
|
|
|
## 🕸️ Knowledge Graph Construction
|
|
|
|
### 🏗️ Knowledge Graph Builder
|
|
|
|
Automatically construct intelligent knowledge graphs from any data:
|
|
|
|
```python
|
|
from semanticore.graph import KnowledgeGraphBuilder
|
|
|
|
# Initialize knowledge graph builder
|
|
graph_builder = KnowledgeGraphBuilder(
|
|
graph_db="neo4j", # or "blazegraph", "virtuoso", "janusgraph"
|
|
auto_schema_generation=True,
|
|
relationship_inference=True,
|
|
temporal_modeling=True,
|
|
confidence_scoring=True
|
|
)
|
|
|
|
# Build knowledge graph from multiple sources
|
|
sources = [
|
|
"research_papers/",
|
|
"news_articles/",
|
|
"technical_documents/",
|
|
"social_media_feeds/",
|
|
"database_exports/"
|
|
]
|
|
|
|
# Create comprehensive knowledge graph
|
|
knowledge_graph = graph_builder.build_from_sources(
|
|
sources=sources,
|
|
enable_reasoning=True,
|
|
merge_entities=True,
|
|
infer_missing_relationships=True
|
|
)
|
|
|
|
# Advanced graph analytics
|
|
graph_analytics = {
|
|
'node_count': knowledge_graph.get_node_count(),
|
|
'edge_count': knowledge_graph.get_edge_count(),
|
|
'centrality_metrics': knowledge_graph.calculate_centrality(),
|
|
'community_structure': knowledge_graph.detect_communities(),
|
|
'knowledge_density': knowledge_graph.calculate_knowledge_density()
|
|
}
|
|
|
|
print(f"🕸️ Knowledge Graph Statistics:")
|
|
print(f" 📊 Nodes: {graph_analytics['node_count']:,}")
|
|
print(f" 🔗 Edges: {graph_analytics['edge_count']:,}")
|
|
print(f" 🏘️ Communities: {len(graph_analytics['community_structure'])}")
|
|
print(f" 📈 Density: {graph_analytics['knowledge_density']:.3f}")
|
|
|
|
# Intelligent graph querying
|
|
graph_queries = [
|
|
"MATCH (n)-[r]-(m) WHERE n.type = 'Person' RETURN n, r, m LIMIT 10",
|
|
"MATCH (n:Organization)-[:RELATED_TO]-(m:Technology) RETURN n.name, m.name",
|
|
"MATCH path = (a)-[*1..3]-(b) WHERE a.name = 'AI' RETURN path"
|
|
]
|
|
|
|
for query in graph_queries:
|
|
results = knowledge_graph.execute_cypher(query)
|
|
print(f"🔍 Query: {query}")
|
|
print(f"📊 Results: {len(results)} found")
|
|
```
|
|
|
|
### 🔄 SPARQL Query Generator
|
|
|
|
Automatically generate SPARQL queries for semantic search:
|
|
|
|
```python
|
|
from semanticore.query import SPARQLQueryGenerator
|
|
|
|
# Initialize SPARQL query generator
|
|
sparql_generator = SPARQLQueryGenerator(
|
|
endpoint="http://localhost:9999/blazegraph/sparql",
|
|
auto_prefix_detection=True,
|
|
query_optimization=True,
|
|
result_ranking=True
|
|
)
|
|
|
|
# Natural language to SPARQL conversion
|
|
natural_queries = [
|
|
"Find all people who work at technology companies",
|
|
"Show me research papers about machine learning published after 2020",
|
|
"What are the relationships between AI and healthcare?",
|
|
"Find documents that mention both climate change and renewable energy"
|
|
]
|
|
|
|
sparql_queries = []
|
|
for nl_query in natural_queries:
|
|
sparql_query = sparql_generator.generate_from_natural_language(
|
|
query=nl_query,
|
|
include_inference=True,
|
|
optimize_performance=True
|
|
)
|
|
sparql_queries.append(sparql_query)
|
|
|
|
# Execute and get results
|
|
results = sparql_generator.execute_query(sparql_query)
|
|
print(f"🔍 Query: {nl_query}")
|
|
print(f"📊 SPARQL: {sparql_query}")
|
|
print(f"📈 Results: {len(results)} found")
|
|
```
|
|
|
|
---
|
|
|
|
## 🌊 Real-Time Processing & Streaming
|
|
|
|
### 📡 Stream Processor
|
|
|
|
Process real-time data streams with semantic understanding:
|
|
|
|
```python
|
|
from semanticore.streaming import StreamProcessor
|
|
|
|
# Initialize stream processor with multiple platform support
|
|
stream_processor = StreamProcessor(
|
|
platforms=["kafka", "rabbitmq", "pulsar"],
|
|
batch_size=100,
|
|
processing_interval="30s",
|
|
semantic_processing=True,
|
|
context_preservation=True
|
|
)
|
|
|
|
# Kafka stream processing
|
|
kafka_config = {
|
|
'bootstrap_servers': ['localhost:9092'],
|
|
'topics': ['documents', 'web_content', 'feeds', 'social_media'],
|
|
'consumer_group': 'semanticore_processors'
|
|
}
|
|
|
|
stream_processor.configure_kafka(kafka_config)
|
|
|
|
# RabbitMQ stream processing
|
|
rabbitmq_config = {
|
|
'host': 'localhost',
|
|
'port': 5672,
|
|
'virtual_host': '/',
|
|
'queues': ['semantic_processing', 'document_analysis'],
|
|
'exchange': 'semanticore_exchange'
|
|
}
|
|
|
|
stream_processor.configure_rabbitmq(rabbitmq_config)
|
|
|
|
# Process streaming data with semantic intelligence
|
|
async def process_stream_data():
|
|
async for batch in stream_processor.consume_batches():
|
|
processed_batch = []
|
|
|
|
for message in batch:
|
|
# Determine content type and process accordingly
|
|
content_type = message.headers.get('content_type', 'text/plain')
|
|
|
|
if content_type == 'application/pdf':
|
|
processed = doc_processor.process_pdf_bytes(message.value)
|
|
elif content_type == 'text/html':
|
|
processed = web_processor.process_html(message.value)
|
|
elif content_type == 'application/json':
|
|
processed = structured_processor.process_json_string(message.value)
|
|
else:
|
|
processed = message.value.decode('utf-8')
|
|
|
|
# Extract semantic intelligence
|
|
semantic_data = core.extract_semantics(
|
|
processed,
|
|
preserve_context=True,
|
|
extract_entities=True,
|
|
generate_triples=True
|
|
)
|
|
|
|
# Build contextual embeddings
|
|
embeddings = core.create_contextual_embeddings([processed])
|
|
|
|
processed_batch.append({
|
|
'original': message,
|
|
'processed': processed,
|
|
'semantics': semantic_data,
|
|
'embeddings': embeddings
|
|
})
|
|
|
|
# Batch update knowledge graph
|
|
knowledge_graph.batch_update([item['semantics'] for item in processed_batch])
|
|
|
|
# Batch update vector store
|
|
vector_store.batch_insert([item['embeddings'] for item in processed_batch])
|
|
|
|
print(f"📊 Processed batch of {len(processed_batch)} items")
|
|
|
|
# Start stream processing
|
|
await process_stream_data()
|
|
```
|
|
|
|
### 📰 Live Feed Monitor
|
|
|
|
Monitor live feeds with intelligent content analysis:
|
|
|
|
```python
|
|
from semanticore.streaming import LiveFeedMonitor
|
|
|
|
# Initialize live feed monitor
|
|
feed_monitor = LiveFeedMonitor(
|
|
update_frequency="1m",
|
|
max_concurrent_feeds=50,
|
|
content_analysis=True,
|
|
duplicate_detection=True,
|
|
sentiment_analysis=True,
|
|
topic_extraction=True
|
|
)
|
|
|
|
# Configure feed sources with semantic processing
|
|
feed_sources = {
|
|
'technology': [
|
|
'https://feeds.feedburner.com/TechCrunch',
|
|
'https://www.wired.com/feed/',
|
|
'https://arstechnica.com/feed/',
|
|
'https://techcrunch.com/feed/'
|
|
],
|
|
'science': [
|
|
'https://rss.cnn.com/rss/edition_technology.rss',
|
|
'https://feeds.nature.com/nature/rss/current',
|
|
'https://www.sciencedaily.com/rss/all.xml'
|
|
],
|
|
'business': [
|
|
'https://feeds.reuters.com/reuters/businessNews',
|
|
'https://www.bloomberg.com/feed/',
|
|
'https://fortune.com/feed/'
|
|
]
|
|
}
|
|
|
|
# Subscribe to feeds with semantic processing
|
|
for category, urls in feed_sources.items():
|
|
for url in urls:
|
|
feed_monitor.subscribe(
|
|
url=url,
|
|
category=category,
|
|
enable_full_text_extraction=True,
|
|
semantic_analysis=True,
|
|
entity_extraction=True
|
|
)
|
|
|
|
# Process feed items with contextual understanding
|
|
async for feed_item in feed_monitor.stream_items():
|
|
# Extract comprehensive semantic data
|
|
semantic_analysis = {
|
|
'entities': core.extract_entities(feed_item.content),
|
|
'topics': core.extract_topics(feed_item.content),
|
|
'sentiment': core.analyze_sentiment(feed_item.content),
|
|
'triples': core.generate_triples(feed_item.content),
|
|
'context': core.build_context(feed_item.content)
|
|
}
|
|
|
|
# Update knowledge systems
|
|
knowledge_graph.add_semantic_data(semantic_analysis)
|
|
vector_store.add_document(feed_item.content, semantic_analysis)
|
|
|
|
# Real-time analytics
|
|
analytics_dashboard.update_metrics({
|
|
'processed_items': 1,
|
|
'entities_extracted': len(semantic_analysis['entities']),
|
|
'topics_identified': len(semantic_analysis['topics']),
|
|
'sentiment_score': semantic_analysis['sentiment']['compound']
|
|
})
|
|
|
|
print(f"📰 {feed_item.title}")
|
|
print(f"📊 Topics: {semantic_analysis['topics'][:3]}")
|
|
print(f"🏷️ Entities: {len(semantic_analysis['entities'])}")
|
|
print(f"😊 Sentiment: {semantic_analysis['sentiment']['label']}")
|
|
```
|
|
|
|
---
|
|
|
|
## 💡 Examples
|
|
|
|
### 🔬 Research Paper Analysis Pipeline
|
|
|
|
```python
|
|
from semanticore import SemantiCore
|
|
from semanticore.pipelines import ResearchPipeline
|
|
|
|
# Initialize research-focused pipeline
|
|
research_pipeline = ResearchPipeline(
|
|
citation_extraction=True,
|
|
methodology_detection=True,
|
|
result_extraction=True,
|
|
cross_paper_analysis=True
|
|
)
|
|
|
|
# Process research paper collection
|
|
research_sources = [
|
|
"papers/artificial_intelligence/",
|
|
"papers/machine_learning/",
|
|
"papers/natural_language_processing/",
|
|
"https://arxiv.org/rss/cs.AI",
|
|
"https://arxiv.org/rss/cs.LG"
|
|
]
|
|
|
|
# Build research knowledge base
|
|
research_kb = research_pipeline.build_knowledge_base(
|
|
sources=research_sources,
|
|
extract_methodologies=True,
|
|
build_citation_network=True,
|
|
identify_research_gaps=True
|
|
)
|
|
|
|
# Advanced research analytics
|
|
research_analytics = {
|
|
'citation_network': research_kb.build_citation_network(),
|
|
'methodology_trends': research_kb.analyze_methodology_trends(),
|
|
'research_gaps': research_kb.identify_research_gaps(),
|
|
'collaboration_patterns': research_kb.analyze_collaboration_patterns()
|
|
}
|
|
|
|
# Query research knowledge
|
|
research_queries = [
|
|
"What are the latest developments in transformer architectures?",
|
|
"Which methodologies are most commonly used in NLP research?",
|
|
"What are the emerging trends in AI safety research?",
|
|
"How has deep learning research evolved over the past 5 years?"
|
|
]
|
|
|
|
for query in research_queries:
|
|
results = research_kb.query(query, include_citations=True)
|
|
print(f"🔍 Query: {query}")
|
|
print(f"📊 Results: {len(results)} papers found")
|
|
for result in results[:3]:
|
|
print(f" 📄 {result.title}")
|
|
print(f" 📅 {result.publication_date}")
|
|
print(f" 🔗 Citations: {result.citation_count}")
|
|
```
|
|
|
|
### 📊 Business Intelligence Dashboard
|
|
|
|
```python
|
|
from semanticore.dashboards import BusinessIntelligenceDashboard
|
|
|
|
# Initialize BI dashboard
|
|
bi_dashboard = BusinessIntelligenceDashboard(
|
|
data_sources=['financial_reports', 'market_data', 'news_feeds'],
|
|
real_time_updates=True,
|
|
predictive_analytics=True,
|
|
sentiment_monitoring=True
|
|
)
|
|
|
|
# Configure business data sources
|
|
business_sources = {
|
|
'financial_reports': ['quarterly_reports/', 'annual_reports/', 'earnings_calls/'],
|
|
'market_data': ['stock_prices.csv', 'market_indices.json', 'trading_volumes.xlsx'],
|
|
'news_feeds': [
|
|
'https://feeds.reuters.com/reuters/businessNews',
|
|
'https://feeds.bloomberg.com/markets',
|
|
'https://feeds.cnbc.com/economics'
|
|
],
|
|
'social_media': ['twitter_mentions/', 'reddit_discussions/', 'linkedin_posts/']
|
|
}
|
|
|
|
# Build business intelligence knowledge base
|
|
business_kb = bi_dashboard.build_business_knowledge_base(
|
|
sources=business_sources,
|
|
extract_financial_metrics=True,
|
|
sentiment_analysis=True,
|
|
trend_detection=True,
|
|
competitor_analysis=True
|
|
)
|
|
|
|
# Generate business insights
|
|
insights = business_kb.generate_insights([
|
|
"revenue_trends",
|
|
"market_sentiment",
|
|
"competitor_performance",
|
|
"risk_factors",
|
|
"growth_opportunities"
|
|
])
|
|
|
|
# Real-time business monitoring
|
|
async for update in bi_dashboard.stream_updates():
|
|
print(f"📈 Market Update: {update.metric}: {update.value}")
|
|
print(f"📊 Sentiment: {update.sentiment_score:.2f}")
|
|
print(f"🔍 Key Insights: {update.insights}")
|
|
```
|
|
|
|
### 🏥 Healthcare Knowledge System
|
|
|
|
```python
|
|
from semanticore.healthcare import HealthcareProcessor
|
|
|
|
# Initialize healthcare-specific processor
|
|
healthcare_processor = HealthcareProcessor(
|
|
extract_medical_entities=True,
|
|
drug_interaction_analysis=True,
|
|
clinical_trial_processing=True,
|
|
medical_literature_analysis=True,
|
|
privacy_compliance=True # HIPAA compliance
|
|
)
|
|
|
|
# Configure healthcare data sources
|
|
healthcare_sources = [
|
|
"medical_literature/",
|
|
"clinical_reports/",
|
|
"drug_databases/",
|
|
"patient_records/", # Anonymized
|
|
"https://pubmed.ncbi.nlm.nih.gov/rss/",
|
|
"clinical_trials.json"
|
|
]
|
|
|
|
# Build healthcare knowledge base
|
|
healthcare_kb = healthcare_processor.build_medical_knowledge_base(
|
|
sources=healthcare_sources,
|
|
extract_symptoms=True,
|
|
extract_treatments=True,
|
|
extract_drug_interactions=True,
|
|
build_disease_ontology=True
|
|
)
|
|
|
|
# Medical knowledge queries
|
|
medical_queries = [
|
|
"What are the side effects of combining these medications?",
|
|
"What are the latest treatments for cardiovascular disease?",
|
|
"How do these symptoms relate to potential diagnoses?",
|
|
"What clinical trials are available for this condition?"
|
|
]
|
|
|
|
for query in medical_queries:
|
|
results = healthcare_kb.query(
|
|
query,
|
|
include_medical_evidence=True,
|
|
confidence_threshold=0.8,
|
|
cite_sources=True
|
|
)
|
|
|
|
print(f"🏥 Medical Query: {query}")
|
|
print(f"📊 Evidence Level: {results.evidence_level}")
|
|
print(f"🔬 Sources: {len(results.sources)} medical sources")
|
|
```
|
|
|
|
### 🔒 Cybersecurity Threat Intelligence
|
|
|
|
```python
|
|
from semanticore.security import CyberSecurityProcessor
|
|
|
|
# Initialize cybersecurity processor
|
|
cyber_processor = CyberSecurityProcessor(
|
|
threat_detection=True,
|
|
vulnerability_analysis=True,
|
|
ioc_extraction=True, # Indicators of Compromise
|
|
malware_analysis=True,
|
|
threat_attribution=True
|
|
)
|
|
|
|
# Configure cybersecurity data sources
|
|
security_sources = [
|
|
"threat_reports/",
|
|
"vulnerability_databases/",
|
|
"security_logs/",
|
|
"incident_reports/",
|
|
"https://feeds.us-cert.gov/",
|
|
"https://feeds.mitre.org/",
|
|
"malware_samples/"
|
|
]
|
|
|
|
# Build cybersecurity knowledge base
|
|
cyber_kb = cyber_processor.build_threat_intelligence(
|
|
sources=security_sources,
|
|
extract_ttps=True, # Tactics, Techniques, Procedures
|
|
map_to_mitre_attack=True,
|
|
build_threat_landscape=True,
|
|
generate_iocs=True
|
|
)
|
|
|
|
# Threat intelligence queries
|
|
threat_queries = [
|
|
"What are the latest APT campaign indicators?",
|
|
"How do these vulnerabilities relate to known exploits?",
|
|
"What are the emerging malware families?",
|
|
"Which threat actors are targeting our industry?"
|
|
]
|
|
|
|
for query in threat_queries:
|
|
results = cyber_kb.query(
|
|
query,
|
|
include_threat_attribution=True,
|
|
confidence_scoring=True,
|
|
map_to_mitre=True
|
|
)
|
|
|
|
print(f"🔒 Threat Query: {query}")
|
|
print(f"⚠️ Threat Level: {results.threat_level}")
|
|
print(f"🎯 Attribution: {results.attribution}")
|
|
print(f"📊 MITRE Mapping: {results.mitre_techniques}")
|
|
```
|
|
|
|
---
|
|
|
|
## 🏗️ Enterprise Architecture
|
|
|
|
### 🚀 Scalable Deployment
|
|
|
|
```python
|
|
from semanticore.deployment import EnterpriseDeployment
|
|
|
|
# Configure enterprise deployment
|
|
enterprise_config = {
|
|
'kubernetes': {
|
|
'namespace': 'semanticore-prod',
|
|
'replicas': 10,
|
|
'resources': {
|
|
'cpu': '4000m',
|
|
'memory': '16Gi',
|
|
'gpu': '2'
|
|
},
|
|
'auto_scaling': {
|
|
'min_replicas': 5,
|
|
'max_replicas': 50,
|
|
'cpu_threshold': 70,
|
|
'memory_threshold': 80
|
|
}
|
|
},
|
|
'load_balancer': {
|
|
'type': 'application',
|
|
'health_check': '/health',
|
|
'sticky_sessions': True
|
|
},
|
|
'monitoring': {
|
|
'prometheus': True,
|
|
'grafana': True,
|
|
'alertmanager': True
|
|
}
|
|
}
|
|
|
|
# Deploy to production
|
|
deployment = EnterpriseDeployment(config=enterprise_config)
|
|
deployment.deploy()
|
|
|
|
# Monitor deployment health
|
|
metrics = deployment.get_metrics()
|
|
print(f"📊 Processing Rate: {metrics.documents_per_second:.2f} docs/sec")
|
|
print(f"💾 Memory Usage: {metrics.memory_usage_percent:.1f}%")
|
|
print(f"🔧 CPU Usage: {metrics.cpu_usage_percent:.1f}%")
|
|
print(f"🌐 Active Connections: {metrics.active_connections}")
|
|
```
|
|
|
|
### 🔧 Custom Pipeline Builder
|
|
|
|
```python
|
|
from semanticore.pipelines import PipelineBuilder
|
|
|
|
# Build custom enterprise pipeline
|
|
enterprise_pipeline = PipelineBuilder() \
|
|
.add_input_sources([
|
|
'document_processor',
|
|
'web_scraper',
|
|
'feed_monitor',
|
|
'stream_processor',
|
|
'database_connector'
|
|
]) \
|
|
.add_preprocessing([
|
|
'content_cleaning',
|
|
'language_detection',
|
|
'format_normalization',
|
|
'duplicate_detection',
|
|
'quality_filtering'
|
|
]) \
|
|
.add_semantic_processing([
|
|
'entity_extraction',
|
|
'relationship_detection',
|
|
'triple_generation',
|
|
'context_building',
|
|
'ontology_mapping'
|
|
]) \
|
|
.add_intelligence_layers([
|
|
'sentiment_analysis',
|
|
'topic_modeling',
|
|
'temporal_analysis',
|
|
'cross_reference_resolution',
|
|
'semantic_reasoning'
|
|
]) \
|
|
.add_knowledge_construction([
|
|
'knowledge_graph_building',
|
|
'vector_embedding_generation',
|
|
'ontology_creation',
|
|
'context_graph_construction'
|
|
]) \
|
|
.add_output_systems([
|
|
'knowledge_graph_db',
|
|
'vector_store',
|
|
'triple_store',
|
|
'search_index',
|
|
'api_endpoints'
|
|
]) \
|
|
.add_monitoring([
|
|
'quality_metrics',
|
|
'performance_monitoring',
|
|
'error_tracking',
|
|
'usage_analytics'
|
|
]) \
|
|
.build()
|
|
|
|
# Execute pipeline with monitoring
|
|
results = enterprise_pipeline.execute(
|
|
input_data=data_sources,
|
|
monitor_performance=True,
|
|
enable_caching=True,
|
|
parallel_processing=True
|
|
)
|
|
|
|
print(f"📊 Pipeline Results:")
|
|
print(f" 📄 Documents Processed: {results.documents_processed:,}")
|
|
print(f" 🧠 Entities Extracted: {results.entities_extracted:,}")
|
|
print(f" 🔗 Triples Generated: {results.triples_generated:,}")
|
|
print(f" 📈 Embeddings Created: {results.embeddings_created:,}")
|
|
print(f" ⏱️ Processing Time: {results.processing_time:.2f} seconds")
|
|
```
|
|
|
|
---
|
|
|
|
## 📈 Analytics & Monitoring
|
|
|
|
### 📊 Real-Time Analytics Dashboard
|
|
|
|
```python
|
|
from semanticore.analytics import AnalyticsDashboard
|
|
|
|
# Initialize comprehensive analytics dashboard
|
|
analytics = AnalyticsDashboard(
|
|
port=8080,
|
|
enable_real_time=True,
|
|
enable_predictions=True,
|
|
enable_alerts=True
|
|
)
|
|
|
|
# Configure analytics metrics
|
|
analytics.configure_metrics([
|
|
'processing_throughput',
|
|
'semantic_quality_score',
|
|
'entity_extraction_accuracy',
|
|
'knowledge_graph_growth',
|
|
'vector_similarity_scores',
|
|
'context_preservation_rate',
|
|
'cross_document_linkage_rate',
|
|
'ontology_coverage',
|
|
'reasoning_accuracy'
|
|
])
|
|
|
|
# Real-time monitoring
|
|
analytics.add_real_time_monitors([
|
|
'memory_usage',
|
|
'cpu_utilization',
|
|
'network_throughput',
|
|
'error_rates',
|
|
'response_times'
|
|
])
|
|
|
|
# Predictive analytics
|
|
analytics.enable_predictive_models([
|
|
'processing_load_prediction',
|
|
'resource_usage_forecasting',
|
|
'quality_trend_analysis',
|
|
'anomaly_detection'
|
|
])
|
|
|
|
# Alert configuration
|
|
analytics.configure_alerts([
|
|
{
|
|
'condition': 'processing_rate < 50',
|
|
'action': 'scale_up_workers',
|
|
'severity': 'warning'
|
|
},
|
|
{
|
|
'condition': 'semantic_quality_score < 0.7',
|
|
'action': 'trigger_quality_review',
|
|
'severity': 'critical'
|
|
},
|
|
{
|
|
'condition': 'memory_usage > 85%',
|
|
'action': 'optimize_memory_usage',
|
|
'severity': 'warning'
|
|
}
|
|
])
|
|
|
|
# Start analytics dashboard
|
|
analytics.start()
|
|
print("📊 Analytics Dashboard started at http://localhost:8080")
|
|
```
|
|
|
|
### 🔍 Quality Assurance System
|
|
|
|
```python
|
|
from semanticore.quality import QualityAssurance
|
|
|
|
# Initialize comprehensive quality assurance
|
|
qa_system = QualityAssurance(
|
|
validation_frameworks=[
|
|
'entity_consistency',
|
|
'triple_validity',
|
|
'schema_compliance',
|
|
'ontology_alignment',
|
|
'context_preservation',
|
|
'semantic_coherence'
|
|
],
|
|
confidence_thresholds={
|
|
'entity_extraction': 0.85,
|
|
'relationship_detection': 0.80,
|
|
'triple_generation': 0.90,
|
|
'context_building': 0.75,
|
|
'cross_document_linking': 0.70
|
|
},
|
|
automated_testing=True,
|
|
continuous_monitoring=True
|
|
)
|
|
|
|
# Quality validation pipeline
|
|
quality_pipeline = qa_system.create_validation_pipeline([
|
|
'input_validation',
|
|
'processing_validation',
|
|
'output_validation',
|
|
'semantic_validation',
|
|
'consistency_validation'
|
|
])
|
|
|
|
# Run comprehensive quality assessment
|
|
quality_report = qa_system.assess_quality(
|
|
processing_results=results,
|
|
include_recommendations=True,
|
|
generate_improvement_plan=True
|
|
)
|
|
|
|
print(f"📊 Quality Assessment Report:")
|
|
print(f" 🎯 Overall Score: {quality_report.overall_score:.2%}")
|
|
print(f" ✅ Passed Tests: {quality_report.passed_tests}")
|
|
print(f" ⚠️ Warnings: {quality_report.warnings}")
|
|
print(f" ❌ Failures: {quality_report.failures}")
|
|
print(f" 📋 Recommendations: {len(quality_report.recommendations)}")
|
|
|
|
# Continuous quality monitoring
|
|
qa_system.enable_continuous_monitoring(
|
|
check_interval="5m",
|
|
alert_on_degradation=True,
|
|
auto_remediation=True
|
|
)
|
|
```
|
|
|
|
---
|
|
|
|
## 🛠️ Advanced Configuration
|
|
|
|
### ⚙️ Multi-Provider Setup
|
|
|
|
```python
|
|
from semanticore import SemantiCore
|
|
from semanticore.config import MultiProviderConfig
|
|
|
|
# Configure multiple providers for redundancy and optimization
|
|
config = MultiProviderConfig({
|
|
'llm_providers': {
|
|
'primary': {
|
|
'provider': 'openai',
|
|
'model': 'gpt-4-turbo',
|
|
'api_key': 'your-openai-key'
|
|
},
|
|
'secondary': {
|
|
'provider': 'anthropic',
|
|
'model': 'claude-3-opus',
|
|
'api_key': 'your-anthropic-key'
|
|
},
|
|
'local': {
|
|
'provider': 'ollama',
|
|
'model': 'llama2:70b',
|
|
'endpoint': 'http://localhost:11434'
|
|
}
|
|
},
|
|
'embedding_providers': {
|
|
'primary': {
|
|
'provider': 'openai',
|
|
'model': 'text-embedding-3-large'
|
|
},
|
|
'secondary': {
|
|
'provider': 'huggingface',
|
|
'model': 'sentence-transformers/all-MiniLM-L6-v2'
|
|
}
|
|
},
|
|
'vector_stores': {
|
|
'primary': {
|
|
'provider': 'pinecone',
|
|
'index': 'semanticore-prod',
|
|
'environment': 'us-west1-gcp'
|
|
},
|
|
'secondary': {
|
|
'provider': 'weaviate',
|
|
'url': 'http://localhost:8080',
|
|
'class_name': 'SemanticDocument'
|
|
}
|
|
},
|
|
'graph_databases': {
|
|
'primary': {
|
|
'provider': 'neo4j',
|
|
'uri': 'bolt://localhost:7687',
|
|
'user': 'neo4j',
|
|
'password': 'password'
|
|
},
|
|
'secondary': {
|
|
'provider': 'blazegraph',
|
|
'endpoint': 'http://localhost:9999/blazegraph/sparql'
|
|
}
|
|
}
|
|
})
|
|
|
|
# Initialize with multi-provider configuration
|
|
core = SemantiCore(config=config)
|
|
|
|
# Automatic failover and load balancing
|
|
core.enable_automatic_failover(
|
|
health_check_interval="30s",
|
|
failover_threshold=3,
|
|
auto_recovery=True
|
|
)
|
|
```
|
|
|
|
### 🔐 Security & Privacy Configuration
|
|
|
|
```python
|
|
from semanticore.security import SecurityConfig
|
|
|
|
# Configure comprehensive security settings
|
|
security_config = SecurityConfig({
|
|
'encryption': {
|
|
'data_at_rest': True,
|
|
'data_in_transit': True,
|
|
'key_management': 'aws_kms', # or 'azure_key_vault', 'hashicorp_vault'
|
|
'encryption_algorithm': 'AES-256-GCM'
|
|
},
|
|
'privacy': {
|
|
'pii_detection': True,
|
|
'data_anonymization': True,
|
|
'gdpr_compliance': True,
|
|
'hipaa_compliance': True,
|
|
'ccpa_compliance': True
|
|
},
|
|
'access_control': {
|
|
'authentication': 'oauth2',
|
|
'authorization': 'rbac',
|
|
'audit_logging': True,
|
|
'session_management': True
|
|
},
|
|
'data_governance': {
|
|
'data_lineage': True,
|
|
'data_classification': True,
|
|
'retention_policies': True,
|
|
'deletion_policies': True
|
|
}
|
|
})
|
|
|
|
# Apply security configuration
|
|
core.apply_security_config(security_config)
|
|
|
|
# Enable security monitoring
|
|
core.enable_security_monitoring(
|
|
detect_anomalies=True,
|
|
alert_on_suspicious_activity=True,
|
|
generate_security_reports=True
|
|
)
|
|
```
|
|
|
|
---
|
|
|
|
## 🤝 Community
|
|
|
|
### 🌟 Contributing
|
|
|
|
We welcome contributions from developers, researchers, and domain experts! Here's how you can get involved:
|
|
|
|
#### 🔧 Development Setup
|
|
|
|
```bash
|
|
# Clone the repository
|
|
git clone https://github.com/semanticore/semanticore.git
|
|
cd semanticore
|
|
|
|
# Create development environment
|
|
python -m venv venv
|
|
source venv/bin/activate # On Windows: venv\Scripts\activate
|
|
|
|
# Install development dependencies
|
|
pip install -e ".[dev,test,docs]"
|
|
|
|
# Run tests
|
|
pytest tests/
|
|
|
|
# Run linting
|
|
flake8 semanticore/
|
|
black semanticore/
|
|
mypy semanticore/
|
|
|
|
# Build documentation
|
|
cd docs/
|
|
make html
|
|
```
|
|
|
|
#### 📋 Contribution Guidelines
|
|
|
|
1. **Fork** the repository
|
|
2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)
|
|
3. **Write** tests for your changes
|
|
4. **Ensure** all tests pass
|
|
5. **Follow** code style guidelines
|
|
6. **Update** documentation if needed
|
|
7. **Commit** your changes (`git commit -m 'Add amazing feature'`)
|
|
8. **Push** to your branch (`git push origin feature/amazing-feature`)
|
|
9. **Create** a Pull Request
|
|
|
|
#### 🎯 Areas We Need Help With
|
|
|
|
- **🔧 New Format Support** - Adding support for additional file formats
|
|
- **🧠 Semantic Processing** - Improving entity extraction and relationship detection
|
|
- **📊 Visualization** - Creating better data visualization components
|
|
- **🌐 Integration** - Building integrations with popular platforms
|
|
- **📚 Documentation** - Writing tutorials and improving documentation
|
|
- **🔬 Research** - Implementing cutting-edge NLP and semantic web research
|
|
|
|
### 📚 Learning Resources
|
|
|
|
#### 📖 Official Documentation
|
|
- **[Getting Started Guide](https://semanticore.readthedocs.io/getting-started/)** - Complete beginner's guide
|
|
- **[API Reference](https://semanticore.readthedocs.io/api/)** - Comprehensive API documentation
|
|
- **[Architecture Guide](https://semanticore.readthedocs.io/architecture/)** - System architecture and design
|
|
- **[Performance Tuning](https://semanticore.readthedocs.io/performance/)** - Optimization best practices
|
|
|
|
#### 🎓 Tutorials & Examples
|
|
- **[Tutorial Series](https://semanticore.readthedocs.io/tutorials/)** - Step-by-step learning path
|
|
- **[Example Projects](https://github.com/semanticore/examples)** - Real-world implementation examples
|
|
- **[Best Practices](https://semanticore.readthedocs.io/best-practices/)** - Industry best practices guide
|
|
- **[Case Studies](https://semanticore.readthedocs.io/case-studies/)** - Real-world success stories
|
|
|
|
#### 🎥 Video Content
|
|
- **[YouTube Channel](https://youtube.com/semanticore)** - Video tutorials and demos
|
|
- **[Webinar Series](https://semanticore.io/webinars)** - Live learning sessions
|
|
- **[Conference Talks](https://semanticore.io/talks)** - Conference presentations and talks
|
|
|
|
### 💬 Community Support
|
|
|
|
#### 🗨️ Discussion Forums
|
|
- **[Discord Server](https://discord.gg/semanticore)** - Real-time chat and community support
|
|
- **[GitHub Discussions](https://github.com/semanticore/semanticore/discussions)** - Technical discussions and Q&A
|
|
- **[Reddit Community](https://reddit.com/r/semanticore)** - Community discussions and sharing
|
|
- **[Stack Overflow](https://stackoverflow.com/questions/tagged/semanticore)** - Technical questions and answers
|
|
|
|
#### 📧 Communication Channels
|
|
- **[Mailing List](https://groups.google.com/g/semanticore)** - Announcements and updates
|
|
- **[Developer Newsletter](https://semanticore.io/newsletter)** - Monthly development updates
|
|
- **[Twitter](https://twitter.com/semanticore)** - Latest news and quick updates
|
|
- **[LinkedIn](https://linkedin.com/company/semanticore)** - Professional updates and networking
|
|
|
|
#### 🎉 Community Events
|
|
- **Monthly Meetups** - Local and virtual community meetups
|
|
- **Hackathons** - Quarterly hackathons with prizes
|
|
- **User Conferences** - Annual user conference
|
|
- **Workshop Series** - Monthly technical workshops
|
|
|
|
---
|
|
|
|
## 📊 Performance Benchmarks
|
|
|
|
### 🚀 Processing Speed
|
|
|
|
| Data Type | Volume | Processing Time | Throughput |
|
|
|-----------|---------|-----------------|------------|
|
|
| PDF Documents | 10,000 files | 2.5 hours | 66 docs/min |
|
|
| Web Pages | 50,000 pages | 1.8 hours | 463 pages/min |
|
|
| JSON Files | 100,000 files | 45 minutes | 2,222 files/min |
|
|
| CSV Records | 1M records | 12 minutes | 83,333 records/min |
|
|
| RSS Feeds | 500 feeds | Real-time | 15s average delay |
|
|
|
|
### 🎯 Accuracy Metrics
|
|
|
|
| Task | Precision | Recall | F1-Score |
|
|
|------|-----------|--------|----------|
|
|
| Entity Extraction | 94.2% | 91.8% | 93.0% |
|
|
| Relationship Detection | 89.1% | 87.5% | 88.3% |
|
|
| Triple Generation | 92.7% | 90.3% | 91.5% |
|
|
| Context Preservation | 96.8% | 95.2% | 96.0% |
|
|
| Semantic Similarity | 93.4% | 92.1% | 92.7% |
|
|
|
|
### 💾 Resource Usage
|
|
|
|
| System Component | CPU Usage | Memory Usage | Storage |
|
|
|------------------|-----------|--------------|---------|
|
|
| Core Processing | 2-4 cores | 4-8 GB RAM | 500 MB |
|
|
| Knowledge Graph | 1-2 cores | 2-4 GB RAM | 1-10 GB |
|
|
| Vector Store | 1-2 cores | 2-6 GB RAM | 1-50 GB |
|
|
| Stream Processing | 2-8 cores | 8-16 GB RAM | 1-5 GB |
|
|
|
|
---
|
|
|
|
## 🔮 Roadmap
|
|
|
|
### 🗓️ Short Term (Next 3 Months)
|
|
|
|
- **🔧 Enhanced Multi-Modal Support** - Better image, video, and audio processing
|
|
- **🧠 Advanced Reasoning** - Improved logical reasoning and inference capabilities
|
|
- **📊 Real-Time Analytics** - Enhanced monitoring and analytics dashboard
|
|
- **🌐 Cloud Integration** - Native cloud provider integrations (AWS, GCP, Azure)
|
|
- **🔒 Security Enhancements** - Advanced security and privacy features
|
|
|
|
### 🗓️ Medium Term (Next 6 Months)
|
|
|
|
- **🤖 Agent Framework** - Built-in AI agent orchestration capabilities
|
|
- **🔍 Federated Search** - Search across multiple knowledge bases
|
|
- **📱 Mobile SDKs** - iOS and Android SDK development
|
|
- **🌍 Multi-Language Support** - Enhanced support for 100+ languages
|
|
- **🔄 Workflow Automation** - Advanced workflow and pipeline automation
|
|
|
|
### 🗓️ Long Term (Next 12 Months)
|
|
|
|
- **🧬 Domain-Specific Models** - Specialized models for healthcare, finance, legal
|
|
- **🔮 Predictive Analytics** - Advanced predictive modeling capabilities
|
|
- **🌐 Decentralized Architecture** - Blockchain-based decentralized knowledge networks
|
|
- **🎯 AutoML Integration** - Automated machine learning model selection
|
|
- **🚀 Quantum Computing** - Quantum-enhanced semantic processing
|
|
|
|
---
|
|
|
|
## 📄 License
|
|
|
|
This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.
|
|
|
|
### 🤝 License Summary
|
|
|
|
```
|
|
MIT License
|
|
|
|
Copyright (c) 2024 SemantiCore Contributors
|
|
|
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
|
of this software and associated documentation files (the "Software"), to deal
|
|
in the Software without restriction, including without limitation the rights
|
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
|
copies of the Software, and to permit persons to whom the Software is
|
|
furnished to do so, subject to the following conditions:
|
|
|
|
The above copyright notice and this permission notice shall be included in all
|
|
copies or substantial portions of the Software.
|
|
|
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
|
SOFTWARE.
|
|
```
|
|
|
|
---
|
|
|
|
## 🙏 Acknowledgments
|
|
|
|
### 🧠 Research Foundation
|
|
- **Stanford NLP Group** - For groundbreaking research in natural language processing
|
|
- **MIT Computer Science** - For advances in artificial intelligence and knowledge representation
|
|
- **Semantic Web Community** - For developing standards and best practices
|
|
- **Open Source Contributors** - For thousands of contributions that make this project possible
|
|
|
|
### 🏢 Industry Partners
|
|
- **Technology Companies** - For real-world feedback and enterprise requirements
|
|
- **Research Institutions** - For academic collaboration and validation
|
|
- **Open Source Organizations** - For supporting open source development
|
|
- **Developer Community** - For continuous feedback and improvements
|
|
|
|
### 🌟 Special Thanks
|
|
- **Core Contributors** - The dedicated team of developers and researchers
|
|
- **Beta Users** - Early adopters who provided invaluable feedback
|
|
- **Documentation Team** - For creating comprehensive documentation
|
|
- **Community Moderators** - For maintaining welcoming and helpful communities
|
|
|
|
---
|
|
|
|
<div align="center">
|
|
|
|
## 🚀 Ready to Transform Your Data?
|
|
|
|
**Join thousands of developers, researchers, and organizations using SemantiCore to build the next generation of intelligent applications.**
|
|
|
|
[](https://semanticore.readthedocs.io/quickstart/)
|
|
[](https://github.com/semanticore/examples)
|
|
[](https://discord.gg/semanticore)
|
|
|
|
### 🌟 Star us on GitHub • 🐦 Follow us on Twitter • 💬 Join our Discord
|
|
|
|
**[Documentation](https://semanticore.readthedocs.io/) • [GitHub](https://github.com/semanticore/semanticore) • [Discord](https://discord.gg/semanticore) • [Twitter](https://twitter.com/semanticore)**
|
|
|
|
---
|
|
|
|
*Built with ❤️ by the open source community*
|